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| import math | |
| from collections import OrderedDict | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| #-------------------------------------------------# | |
| # MISH激活函数 | |
| #-------------------------------------------------# | |
| class Mish(nn.Module): | |
| def __init__(self): | |
| super(Mish, self).__init__() | |
| def forward(self, x): | |
| return x * torch.tanh(F.softplus(x)) | |
| #---------------------------------------------------# | |
| # 卷积块 -> 卷积 + 标准化 + 激活函数 | |
| # Conv2d + BatchNormalization + Mish | |
| #---------------------------------------------------# | |
| class BasicConv(nn.Module): | |
| def __init__(self, in_channels, out_channels, kernel_size, stride=1): | |
| super(BasicConv, self).__init__() | |
| self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, kernel_size//2, bias=False) | |
| self.bn = nn.BatchNorm2d(out_channels) | |
| self.activation = Mish() | |
| def forward(self, x): | |
| x = self.conv(x) | |
| x = self.bn(x) | |
| x = self.activation(x) | |
| return x | |
| #---------------------------------------------------# | |
| # CSPdarknet的结构块的组成部分 | |
| # 内部堆叠的残差块 | |
| #---------------------------------------------------# | |
| class Resblock(nn.Module): | |
| def __init__(self, channels, hidden_channels=None): | |
| super(Resblock, self).__init__() | |
| if hidden_channels is None: | |
| hidden_channels = channels | |
| self.block = nn.Sequential( | |
| BasicConv(channels, hidden_channels, 1), | |
| BasicConv(hidden_channels, channels, 3) | |
| ) | |
| def forward(self, x): | |
| return x + self.block(x) | |
| #--------------------------------------------------------------------# | |
| # CSPdarknet的结构块 | |
| # 首先利用ZeroPadding2D和一个步长为2x2的卷积块进行高和宽的压缩 | |
| # 然后建立一个大的残差边shortconv、这个大残差边绕过了很多的残差结构 | |
| # 主干部分会对num_blocks进行循环，循环内部是残差结构。 | |
| # 对于整个CSPdarknet的结构块，就是一个大残差块+内部多个小残差块 | |
| #--------------------------------------------------------------------# | |
| class Resblock_body(nn.Module): | |
| def __init__(self, in_channels, out_channels, num_blocks, first): | |
| super(Resblock_body, self).__init__() | |
| #----------------------------------------------------------------# | |
| # 利用一个步长为2x2的卷积块进行高和宽的压缩 | |
| #----------------------------------------------------------------# | |
| self.downsample_conv = BasicConv(in_channels, out_channels, 3, stride=2) | |
| if first: | |
| #--------------------------------------------------------------------------# | |
| # 然后建立一个大的残差边self.split_conv0、这个大残差边绕过了很多的残差结构 | |
| #--------------------------------------------------------------------------# | |
| self.split_conv0 = BasicConv(out_channels, out_channels, 1) | |
| #----------------------------------------------------------------# | |
| # 主干部分会对num_blocks进行循环，循环内部是残差结构。 | |
| #----------------------------------------------------------------# | |
| self.split_conv1 = BasicConv(out_channels, out_channels, 1) | |
| self.blocks_conv = nn.Sequential( | |
| Resblock(channels=out_channels, hidden_channels=out_channels//2), | |
| BasicConv(out_channels, out_channels, 1) | |
| ) | |
| self.concat_conv = BasicConv(out_channels*2, out_channels, 1) | |
| else: | |
| #--------------------------------------------------------------------------# | |
| # 然后建立一个大的残差边self.split_conv0、这个大残差边绕过了很多的残差结构 | |
| #--------------------------------------------------------------------------# | |
| self.split_conv0 = BasicConv(out_channels, out_channels//2, 1) | |
| #----------------------------------------------------------------# | |
| # 主干部分会对num_blocks进行循环，循环内部是残差结构。 | |
| #----------------------------------------------------------------# | |
| self.split_conv1 = BasicConv(out_channels, out_channels//2, 1) | |
| self.blocks_conv = nn.Sequential( | |
| *[Resblock(out_channels//2) for _ in range(num_blocks)], | |
| BasicConv(out_channels//2, out_channels//2, 1) | |
| ) | |
| self.concat_conv = BasicConv(out_channels, out_channels, 1) | |
| def forward(self, x): | |
| x = self.downsample_conv(x) | |
| x0 = self.split_conv0(x) | |
| x1 = self.split_conv1(x) | |
| x1 = self.blocks_conv(x1) | |
| #------------------------------------# | |
| # 将大残差边再堆叠回来 | |
| #------------------------------------# | |
| x = torch.cat([x1, x0], dim=1) | |
| #------------------------------------# | |
| # 最后对通道数进行整合 | |
| #------------------------------------# | |
| x = self.concat_conv(x) | |
| return x | |
| #---------------------------------------------------# | |
| # CSPdarknet53 的主体部分 | |
| # 输入为一张416x416x3的图片 | |
| # 输出为三个有效特征层 | |
| #---------------------------------------------------# | |
| class CSPDarkNet(nn.Module): | |
| def __init__(self, layers): | |
| super(CSPDarkNet, self).__init__() | |
| self.inplanes = 32 | |
| # 416,416,3 -> 416,416,32 | |
| self.conv1 = BasicConv(3, self.inplanes, kernel_size=3, stride=1) | |
| self.feature_channels = [64, 128, 256, 512, 1024] | |
| self.stages = nn.ModuleList([ | |
| # 416,416,32 -> 208,208,64 | |
| Resblock_body(self.inplanes, self.feature_channels[0], layers[0], first=True), | |
| # 208,208,64 -> 104,104,128 | |
| Resblock_body(self.feature_channels[0], self.feature_channels[1], layers[1], first=False), | |
| # 104,104,128 -> 52,52,256 | |
| Resblock_body(self.feature_channels[1], self.feature_channels[2], layers[2], first=False), | |
| # 52,52,256 -> 26,26,512 | |
| Resblock_body(self.feature_channels[2], self.feature_channels[3], layers[3], first=False), | |
| # 26,26,512 -> 13,13,1024 | |
| Resblock_body(self.feature_channels[3], self.feature_channels[4], layers[4], first=False) | |
| ]) | |
| self.num_features = 1 | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels | |
| m.weight.data.normal_(0, math.sqrt(2. / n)) | |
| elif isinstance(m, nn.BatchNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = self.stages[0](x) | |
| x = self.stages[1](x) | |
| out3 = self.stages[2](x) | |
| out4 = self.stages[3](out3) | |
| out5 = self.stages[4](out4) | |
| return out3, out4, out5 | |
| def darknet53(pretrained): | |
| model = CSPDarkNet([1, 2, 8, 8, 4]) | |
| if pretrained: | |
| model.load_state_dict(torch.load("model_data/CSPdarknet53_backbone_weights.pth")) | |
| return model | |